University of Virginia Landscape Architecture Chair Bradley Cantrell, ASLA, sees the future of landscape design as a spectrum of interactions between technologies that sense the environment, model and simulate it, and then finally affect the physical world—all without constant human input and monitoring. As argued in his March 13 LAM Lecture (and in his recent book Responsive Landscapes, written with Justine Holzman, ASLA), the future of landscape architecture is one of designing protocols for how natural systems behave, and tuning these algorithms and eventually the land itself, thus loosening the stranglehold static and monofunctional infrastructure has on the planet. “It’s not about us controlling every aspect,” he says. “It’s about us setting a range of ways those behaviors can act within.”

Cantrell’s research is grounded in the previous century’s cutting-edge modeling and simulation methods, like the Army Corp of Engineers Mississippi River Basin Model in Clinton, Mississippi, which modeled the entire rivershed, scaled down to a mere 200 acres. From there, Cantrell details contemporary research that is equal parts computational and material, honing ever more granular data points toward more accurate models. For example, there’s USC Assistant Professor Alexander Robinson’sOffice of Outdoor Research, Landscape Morphologies Lab work, which uses an articulated robot arm to scrape out dust-mitigating landforms at California’s Owens Lake. Cantrell’s own inquiries involve test bed river basin models that deposit sediment via the variable flow of water, which he has been able to manipulate as though it were a geologic 3-D printer, expanding and cutting back sediment deposit “land” where it’s desired. The resulting topographies are scanned and converted into point-cloud maps.

Cantrell’s approach pushes landscape architecture’s prevailing infrastructure fixation until it ricochets out of the physically imposing world of concrete and culverts and into abstract data, underpinning the omnipresent ways we reengineer ecologies with quantitative facts. The biggest challenge for modeling and simulating dynamic environments, Cantrell says, is not gathering all the requisite data, but getting it to interact in a way that matches reality. At its core, it’s a call for new levels of observational rigor: first, to observe all the factors that make an ecosystem function, and then to understand how those factors work together to create a landscape.